HYBRID DEEP LEARNING MODELS FOR MULTILINGUAL SENTIMENT ANALYSIS IN LOW-RESOURCE LANGUAGES

Multilingual sentiment analysis poses significant challenges, especially in the context of languages with low resources. The study proposes a hybrid deep learning model based on the CNN-BiLSTM architecture to classify sentiment in multiple languages, including those with limited corpus and lexical resources. This model integrates the multilingual text representation of mBERT embedding with CNN's ability to extract local features and BiLSTM's power in capturing sequential contexts. Experiments were conducted on datasets that included various languages such as Indonesian, Hausa, Swahili, and Yoruba. The results of the evaluation showed that the proposed model achieved an accuracy of 84.3% and a macro F1 score of 83.1%, outperforming basic models such as Naive Bayes and independent BiLSTM. 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